Spike Count Research Articles

Modeling of Activity-Induced Changes in Signal Propagation Speed of Mechano-Electrically Stimulated Nerve Fiber.

One of the important questions in the research on neural coding is how the preceding axonal activity affects the signal propagation speed of the following one. We present an approach to solving this problem by introducing a multi-level spike count for activity quantification and fitting a family of linear regression models to the data. The best-achieved score is R2=0.89 and the comparison of different models indicates the importance of long and very short nerve fiber memory. Further studies are required to understand the complex axonal mechanisms responsible for the discovered phenomena.

Delta oscillation underlies the interictal spike changes after repeated transcranial direct current stimulation in a rat model of chronic seizures

BackgroundTranscranial direct current stimulation (tDCS) provides a noninvasive polarity-specific constant current to treat epilepsy, through a mechanism possibly involving excitability modulation and neural oscillation. ObjectiveTo determine whether EEG oscillations underlie the interictal spike changes after tDCS in rats with chronic spontaneous seizures. MethodsRats with kainic acid-induced spontaneous seizures were subjected to cathodal tDCS or sham stimulation for 5 consecutive days. Video-EEG recordings were collected immediately pre- and post-stimulation and for the subsequent 2 weeks following stimulation. The acute pre-post stimulation and subacute follow-up changes of interictal spikes and EEG oscillations in tDCS-treated rats were compared with sham. Ictal EEG with seizure behaviors, hippocampal brain-derived neurotrophic factor (BDNF) protein expression, and mossy fiber sprouting were compared between tDCS and sham rats. ResultsInterictal spike counts were reduced immediately following tDCS with augmented delta and diminished beta and gamma oscillations compared with sham. Cathodal tDCS also enhanced delta oscillations in normal rats. However, increased numbers of interictal spikes with a decrease of delta and theta oscillations were observed in tDCS-treated rats compared with sham during the following 2 weeks after stimulation. Resuming tDCS suppressed the increase of interictal spike activity. In tDCS rats, hippocampal BDNF protein expression was decreased while mossy fiber sprouting did not change compared with sham. ConclusionsThe inverse relationship between the changes of delta oscillation and interictal spikes during tDCS on and off stimulation periods indicates that an enhanced endogenous delta oscillation underlies the tDCS inhibitory effect on epileptic excitability.

Pinpointing the neural signatures of single-exposure visual recognition memory

Memories of the images that we have seen are thought to be reflected in the reduction of neural responses in high-level visual areas such as inferotemporal (IT) cortex, a phenomenon known as repetition suppression (RS). We challenged this hypothesis with a task that required rhesus monkeys to report whether images were novel or repeated while ignoring variations in contrast, a stimulus attribute that is also known to modulate the overall IT response. The monkeys' behavior was largely contrast invariant, contrary to the predictions of an RS-inspired decoder, which could not distinguish responses to images that are repeated from those that are of lower contrast. However, the monkeys' behavioral patterns were well predicted by a linearly decodable variant in which the total spike count was corrected for contrast modulation. These results suggest that the IT neural activity pattern that best aligns with single-exposure visual recognition memory behavior is not RS but rather sensory referenced suppression: reductions in IT population response magnitude, corrected for sensory modulation.

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code.

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.

Species identification and morphological trait diversity assessment in ryegrass (Lolium spp.) populations from the Texas Blackland Prairies

AbstractRyegrass (Lolium spp.) is a troublesome weed in major wheat (Triticum aestivum L.) production regions in the United States. High diversity and adaptive potential are known to contribute to its success as a weed species and also create difficulties in correct species identification in fields. The objective of this research was to characterize diversity for 16 different morphological traits among 56 Lolium populations collected from wheat production fields across the Texas Blackland Prairies region and identify Lolium species based on taxonomic characteristics. Populations were highly diverse (both at inter- and intrapopulation levels) for the traits studied, and a taxonomic comparison with USDA-GRIN reference samples revealed that all the populations were variants of Italian ryegrass [Lolium perenne L. ssp. multiflorum (Lam.) Husnot] with a few offtypes of perennial ryegrass (Lolium perenne L.) or probable hybrids between the two species. Hierarchical clustering grouped the populations into six clusters based on their similarities for the morphological traits investigated. Principal component analysis showed that the variability for yield traits greatly contributed to the total diversity. Pre-flowering plant height (stage 10 on Feekes scale) was positively correlated with tiller count, shoot biomass, and spike count, but not with total seed count per plant, whereas plant height at maturity (stage 11.3 to 11.4 on Feekes scale) was highly correlated with total seeds per plant. Further, basal node color was positively correlated with plant growth habit, regrowth rate, and leaf color. Leaf blade width was positively correlated with survival to pinoxaden and multiple herbicides, whereas, spike count was negatively correlated with survival to mesosulfuron. The high levels of intra- as well as interpopulation variability documented in this study indicate the potential of this species to rapidly adapt to herbicides and emphasize the need for implementing diverse management tactics, including the integration of harvest weed seed control.

Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals.

Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We analyze a phenomenon in these models where such statistically dependent ISI sequences arise in tandem with quasi-statistically independent and identically distributed (quasi-IID) adaptation variable sequences. The sequences of adaptation states and resulting ISIs are linked by a nonlinear decorrelating transformation. We establish general conditions on a family of stochastic spiking models that guarantee this quasi-IID property and establish bounds on the resulting baseline ISI correlations. Inputs that elicit weak firing rate changes in samples with many spikes are known to be more detectible when negative ISI correlations are present because they reduce spike count variance; this defines a variance-reduced firing rate coding benchmark. We performed a Fisher information analysis on these adapting models exhibiting ISI correlations to show that a spike pattern code based on the quasi-IID property achieves the upper bound of detection performance, surpassing rate codes with the same mean rate-including the variance-reduced rate code benchmark-by 20% to 30%. The information loss in rate codes arises because the benefits of reduced spike count variance cannot compensate for the lower firing rate gain due to adaptation. Since adaptation states have similar dynamics to synaptic responses, the quasi-IID decorrelation transformation of the spike train is plausibly implemented by downstream neurons through matched postsynaptic kinetics. This provides an explanation for observed coding performance in sensory systems that cannot be accounted for by rate coding, for example, at the detection threshold where rate changes can be insignificant.

Temporal Encoding is Required for Categorization, But Not Discrimination.

Core auditory cortex (AC) neurons encode slow fluctuations of acoustic stimuli with temporally patterned activity. However, whether temporal encoding is necessary to explain auditory perceptual skills remains uncertain. Here, we recorded from gerbil AC neurons while they discriminated between a 4-Hz amplitude modulation (AM) broadband noise and AM rates >4Hz. We found a proportion of neurons possessed neural thresholds based on spike pattern or spike count that were better than the recorded session's behavioral threshold, suggesting that spike count could provide sufficient information for this perceptual task. A population decoder that relied on temporal information outperformed a decoder that relied on spike count alone, but the spike count decoder still remained sufficient to explain average behavioral performance. This leaves open the possibility that more demanding perceptual judgments require temporal information. Thus, we asked whether accurate classification of different AM rates between 4 and 12Hz required the information contained in AC temporal discharge patterns. Indeed, accurate classification of these AM stimuli depended on the inclusion of temporal information rather than spike count alone. Overall, our results compare two different representations of time-varying acoustic features that can be accessed by downstream circuits required for perceptual judgments.

Task-specific modulation of PFC activity for matching-rule governed decision-making.

Storing information from incoming stimuli in working memory (WM) is essential for decision-making. The prefrontal cortex (PFC) plays a key role to support this process. Previous studies have characterized different neuronal populations in the PFC for working memory judgements based on whether an originally presented stimulus matches a subsequently presented one (matching-rule decision-making). However, much remains to be understood about this mechanism at the population level of PFC neurons. Here, we hypothesized differences in processing of feature vs. spatial WM within the PFC during a matching-rule decision-making task. To test this hypothesis, the modulation of neural activity within the PFC during two types of decision-making tasks (spatial WM and feature WM) in comparison to a passive fixation task was determined. We discovered that neural population-level activity within the PFC is different for the match vs. non-match condition exclusively in the case of the feature-specific decision-making task. For this task, the non-match condition exhibited a greater firing rate and lower trial-to-trial variability in spike count compared to the feature-match condition. Furthermore, the feature-match condition exhibited lower variability compared to the spatial-match condition. This was accompanied by a faster behavioral response time for the feature-match compared to the spatial-match WM task. We attribute this lower across-trial spiking variability and behavioral response time to a higher task-relevant attentional level in the feature WM compared to the spatial WM task. The findings support our hypothesis for task-specific differences in the processing of feature vs. spatial WM within the PFC. This also confirms the general conclusion that PFC neurons play an important role during the process of matching-rule governed decision-making.

Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants

Computer vision with deep learning is emerging as a significant approach for non-invasive and non-destructive plant phenotyping. Spikes are the reproductive organs of wheat plants. Detection and counting of spikes considered the grain-bearing organ have great importance in the phenomics study of large sets of germplasms. In the present study, we developed an online platform, “Web-SpikeSegNet,” based on a deep-learning framework for spike detection and counting from the wheat plant’s visual images. The architecture of the Web-SpikeSegNet consists of 2 layers. First Layer, Client-Side Interface Layer, deals with end user’s requests and corresponding responses management. In contrast, the second layer, Server Side Application Layer, consists of a spike detection and counting module. The backbone of the spike detection module comprises of deep encoder-decoder network with hourglass network for spike segmentation. The Spike counting module implements the “Analyze Particle” function of imageJ to count the number of spikes. For evaluating the performance of Web-SpikeSegNet, we acquired the wheat plant’s visual images, and the satisfactory segmentation performances were obtained as Type I error 0.00159, Type II error 0.0586, Accuracy 99.65%, Precision 99.59% and F1 score 99.65%. As spike detection and counting in wheat phenotyping are closely related to the yield, Web-SpikeSegNet is a significant step forward in the field of wheat phenotyping and will be very useful to the researchers and students working in the domain.

An Efficient and Flexible Spike Train Model Via Empirical Bayes

Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neurons demonstrate over-dispersed spiking behaviour. Although the Negative Binomial Generalized Linear Model (NB-GLM) provides a powerful tool for modeling over-dispersed spike counts, the maximum likelihood-based standard NB-GLM leads to highly variable and inaccurate parameter estimates. Thus, we propose a hierarchical parametric empirical Bayes method to estimate the neural spike responses among neuronal population. Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations. We apply our approach to study both simulated data and experimental neural data. The estimation of simulation data indicates that the new framework can accurately predict mean spike counts simulated from different models and recover the connectivity weights among neural populations. The estimation based on retinal neurons demonstrate the proposed method outperforms both NB-GLM and Poisson-GLM in terms of the predictive log-likelihood of held-out data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Spiking Characteristics of Network-Mediated Responses Arising in Direction-Selective Ganglion Cells of Rabbit and Mouse Retinas to Electric Stimulation for Retinal Prostheses.

To restore the sight of individuals blinded by outer retinal degeneration, numerous retinal prostheses have been developed. However, the performance of those implants is still hampered by some factors including the lack of comprehensive understanding of the electrically-evoked responses arising in various retinal ganglion cell (RGC) types. In this study, we characterized the electrically-evoked network-mediated responses (hereafter referred to as electric responses) of ON-OFF direction-selective (DS) RGCs in rabbit and mouse retinas for the first time. Interestingly, both species in common demonstrated strong negative correlations between spike counts of electric responses and direction selective indices (DSIs), suggesting electric stimulation activates inhibitory presynaptic neurons that suppress null direction responses for high direction tuning in their light responses. The DS cells of the two species showed several differences including different numbers of bursts. Also, spiking patterns were more heterogeneous across DS RGCs of rabbits than those of mice. The electric response magnitudes of rabbit DS cells showed positive and negative correlations with ON and OFF light response magnitudes to preferred direction motion, respectively. But the mouse DS cells showed positive correlations in both comparisons. Our Fano Factor (FF) and spike time tiling coefficient (STTC) analyses revealed that spiking consistencies across repeats were reduced in late electric responses in both species. Moreover, the response consistencies of DS RGCs were lower than those of non-DS RGCs. Our results indicate the species-dependent retinal circuits may result in different electric response features and therefore suggest a proper animal model may be crucial in prosthetic researches.

Enhancing GABAergic Tone in the Rostral Nucleus of the Solitary Tract Reconfigures Sensorimotor Neural Activity.

Recent work has shown that most cells in the rostral, gustatory portion of the nucleus tractus solitarius (rNTS) in awake, freely licking rats show lick-related firing. However, the relationship between taste-related and lick-related activity in rNTS remains unclear. Here, we tested whether GABA-derived inhibitory activity regulates the balance of lick- and taste-driven neuronal activity. Combinatorial viral tools were used to restrict the expression of channelrhodopsin 2-enhanced yellow fluorescent protein to GAD1+ GABAergic neurons. Viral infusions were bilateral in rNTS. A fiber-optic fiber attached to a bundle of drivable microwires was later implanted into the rNTS. After recovery, water-deprived rats were presented with taste stimuli in an experimental chamber. Trials were five consecutive taste licks [NaCl, KCl, NH4Cl, sucrose, monosodium glutamate/inosine-5'-monophosphate, citric acid, quinine, or artificial saliva (AS)] separated by five AS rinse licks on a variable ratio 5 schedule. Each taste lick triggered a 1 s train of laser light (25 Hz; 473 nm; 8-10 mW) in a random half of the trials. In all, 113 cells were recorded in the rNTS, 50 cells responded to one or more taste stimuli without GABA enhancement. Selective changes in response magnitude (spike count) within cells shifted across-unit patterns but preserved interstimulus relationships. Cells where enhanced GABAergic tone increased lick coherence conveyed more information distinguishing basic taste qualities and different salts than other cells. In addition, GABA activation significantly amplified the amount of information that discriminated palatable versus unpalatable tastants. By dynamically regulating lick coherence and remodeling the across-unit response patterns to taste, enhancing GABAergic tone in rNTS reconfigures the neural activity reflecting sensation and movement.

Poisson balanced spiking networks.

An important problem in computational neuroscience is to understand how networks of spiking neurons can carry out various computations underlying behavior. Balanced spiking networks (BSNs) provide a powerful framework for implementing arbitrary linear dynamical systems in networks of integrate-and-fire neurons. However, the classic BSN model requires near-instantaneous transmission of spikes between neurons, which is biologically implausible. Introducing realistic synaptic delays leads to an pathological regime known as “ping-ponging”, in which different populations spike maximally in alternating time bins, causing network output to overshoot the target solution. Here we document this phenomenon and provide a novel solution: we show that a network can have realistic synaptic delays while maintaining accuracy and stability if neurons are endowed with conditionally Poisson firing. Formally, we propose two alternate formulations of Poisson balanced spiking networks: (1) a “local” framework, which replaces the hard integrate-and-fire spiking rule within each neuron by a “soft” threshold function, such that firing probability grows as a smooth nonlinear function of membrane potential; and (2) a “population” framework, which reformulates the BSN objective function in terms of expected spike counts over the entire population. We show that both approaches offer improved robustness, allowing for accurate implementation of network dynamics with realistic synaptic delays between neurons. Both Poisson frameworks preserve the coding accuracy and robustness to neuron loss of the original model and, moreover, produce positive correlations between similarly tuned neurons, a feature of real neural populations that is not found in the deterministic BSN. This work unifies balanced spiking networks with Poisson generalized linear models and suggests several promising avenues for future research.

Fano Factor: A Potentially Useful Information.

The Fano factor, defined as the variance-to-mean ratio of spike counts in a time window, is often used to measure the variability of neuronal spike trains. However, despite its transparent definition, careless use of the Fano factor can easily lead to distorted or even wrong results. One of the problems is the unclear dependence of the Fano factor on the spiking rate, which is often neglected or handled insufficiently. In this paper we aim to explore this problem in more detail and to study the possible solution, which is to evaluate the Fano factor in the operational time. We use equilibrium renewal and Markov renewal processes as spike train models to describe the method in detail, and we provide an illustration on experimental data.

Frontal eye field inactivation alters the readout of superior colliculus activity for saccade generation in a task-dependent manner.

Saccades require a spatiotemporal transformation of activity between the intermediate layers of the superior colliculus (iSC) and downstream brainstem burst generator. The dynamic linear ensemble-coding model (Goossens and Van Opstal 2006) proposes that each iSC spike contributes a fixed mini-vector to saccade displacement. Although biologically-plausible, this model assumes cortical areas like the frontal eye fields (FEF) simply provide the saccadic goal to be executed by the iSC and brainstem burst generator. However, the FEF and iSC operate in unison during saccades, and a pathway from the FEF to the brainstem burst generator that bypasses the iSC exists. Here, we investigate the impact of large yet reversible inactivation of the FEF on iSC activity in the context of the model across four saccade tasks. We exploit the overlap of saccade vectors generated when the FEF is inactivated or not, comparing the number of iSC spikes for metrically-matched saccades. We found that the iSC emits fewer spikes for metrically-matched saccades during FEF inactivation. The decrease in spike count is task-dependent, with a greater decrease accompanying more cognitively-demanding saccades. Our results show that FEF integrity influences the readout of iSC activity in a task-dependent manner. We propose that the dynamic linear ensemble-coding model be modified so that FEF inactivation increases the gain of a readout parameter, effectively increasing the influence of a single iSC spike. We speculate that this modification could be instantiated by FEF and iSC pathways to the cerebellum that could modulate the excitability of the brainstem burst generator.

Independent representations of self-motion and object location in barrel cortex output.

During active tactile exploration, the dynamic patterns of touch are transduced to electrical signals and transformed by the brain into a mental representation of the object under investigation. This transformation from sensation to perception is thought to be a major function of the mammalian cortex. In primary somatosensory cortex (S1) of mice, layer 5 (L5) pyramidal neurons are major outputs to downstream areas that influence perception, decision-making, and motor control. We investigated self-motion and touch representations in L5 of S1 with juxtacellular loose-seal patch recordings of optogenetically identified excitatory neurons. We found that during rhythmic whisker movement, 54 of 115 active neurons (47%) represented self-motion. This population was significantly more modulated by whisker angle than by phase. Upon active touch, a distinct pattern of activity was evoked across L5, which represented the whisker angle at the time of touch. Object location was decodable with submillimeter precision from the touch-evoked spike counts of a randomly sampled handful of these neurons. These representations of whisker angle during self-motion and touch were independent, both in the selection of which neurons were active and in the angle-tuning preference of coactive neurons. Thus, the output of S1 transiently shifts from a representation of self-motion to an independent representation of explored object location during active touch.

Evaluation of Biochar and Charcoal As Amendments and Their Effect on Sandy Soil Fertility and Wheat Productivity تقیم تاثیر کل من الفحم النباتى والفحم الحیوى على خصوبه الارض الرملیة وانتاجیة القمح

Two field experiments were conducted during two consecutive winter seasons. 2017/2018 and 2018/2019 to assess charcoal and biochar on soil fertility and wheat productivity in sandy soils at the Agriculture Station, Ismailia Governorate, Agricultural Research Center, Egypt. , Each trial was performed in a randomized, complete system fashion with three replicates. Charcoal used for application rates (0, 1 and 2 tons acre -1) was mixed with soil 20 days before planting. Biological fertilization, Rhizobium radiobacter (PGPR), was applied by coating wheat kernels (Triticum aestivum L.) Giza 171. The results indicated that the soil pH value ranged from 7.94 to 7.79 for soils treated with charcoal, while these values were. It was 7.94 to 7.81 for biochar treated soils. Also, ECe values tend to be lower with the application of charcoal, as the available micro and macronutrients have been increased due to the use of both biochar or charcoal. The values of all cultivated characteristics of wheat such as plant height (cm), spike length (cm), spike count, 1000 grain weight (g), and straw and grain yield weight (ton / acre) increased with the increase. The rate of charcoal compared to biochar. The increase in the concentration of macronutrients and micronutrients in the grain and straw of wheat plants may be due to the increased availability of nutrients in the soil as they are affected by charcoal compared to biochar. It can be concluded that charcoal at a rate of 2 tons/acre achieved monster results compared to biochar and increased wheat yield.

Mice Preferentially Use Increases in Cerebral Cortex Spiking to Detect Changes in Visual Stimuli.

Whenever the retinal image changes, some neurons in visual cortex increase their rate of firing whereas others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice of both sexes to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements in spiking were generated in the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity has costs. This work clarifies neuronal decoding strategies used by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.SIGNIFICANCE STATEMENT Visual responses in the primary visual cortex (V1) are diverse, in that neurons can be either excited or inhibited by the onset of a visual stimulus. We selectively potentiated or suppressed V1 spiking in mice while they performed contrast change detection tasks. In other experiments, excitation or inhibition was delivered to V1 independent of visual stimuli. Mice readily detected increases in V1 spiking while equivalent reductions in V1 spiking suppressed the probability of detection, even when increases and decreases in V1 spiking were generated in the same neuronal populations. Our data raise the striking possibility that only increments in spiking are used to render information to structures downstream of V1.

Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning

To solve real-time challenges, neuromorphic systems generally require deep and complex network structures. Thus, it is crucial to search for effective solutions that can reduce network complexity, improve energy efficiency, and maintain high accuracy. To this end, we propose unsupervised pruning strategies that are focused on pruning neurons while training in spiking neural networks (SNNs) by utilizing network dynamics. The importance of neurons is determined by the fact that neurons that fire more spikes contribute more to network performance. Based on these criteria, we demonstrate that pruning with an adaptive spike count threshold provides a simple and effective approach that can reduce network size significantly and maintain high classification accuracy. The online adaptive pruning shows potential for developing energy-efficient training techniques due to less memory access and less weight-update computation. Furthermore, a parallel digital implementation scheme is proposed to implement spiking neural networks (SNNs) on field programmable gate array (FPGA). Notably, our proposed pruning strategies preserve the dense format of weight matrices, so the implementation architecture remains the same after network compression. The adaptive pruning strategy enables 2.3× reduction in memory size and 2.8× improvement on energy efficiency when 400 neurons are pruned from an 800-neuron network, while the loss of classification accuracy is 1.69%. And the best choice of pruning percentage depends on the trade-off among accuracy, memory, and energy. Therefore, this work offers a promising solution for effective network compression and energy-efficient hardware implementation of neuromorphic systems in real-time applications.

Retinal Degeneration Reduces Consistency of Network-Mediated Responses Arising in Ganglion Cells to Electric Stimulation.

Retinal prostheses use periodic repetition of electrical stimuli to form artificial vision. To enhance the reliability of evoked visual percepts, repeating stimuli need to evoke consistent spiking activity in individual retinal ganglion cells (RGCs). However, it is not well known whether outer retinal degeneration alters the consistency of RGC responses. Hence, here we systematically investigated the trial-to-trial variability in network-mediated responses as a function of the degeneration level. We patch-clamp recorded spikes in ON and OFF types of alpha RGCs from r d10 mice at four different postnatal days (P15, P19, P31, and P60), representing distinct stages of degeneration. To assess the consistency of responses, we analyzed variances in spike count and timing across repeats of the same stimulus delivered multiple times. We found the trial-to-trial variability of network-mediated responses increased considerably as the disease progressed. Compared to responses taken before degeneration onset, those of degenerate retinas showed up to ~70% higher variability (Fano Factor) in spike counts (p < 0.001) and ~95% lower correlation level in spike timing (p < 0.001). These results indicate consistency weakens significantly in electrically-evoked network-mediated responses and therefore raise concerns about the ability of microelectronic retinal implants to elicit consistent visual percepts at advanced stages of retinal degeneration.